Add a TF in-graph tokenizer for BERT (#17701)

* Add a TF in-graph tokenizer for BERT

* Add from_pretrained

* Add proper truncation, option handling to match other tokenizers

* Add proper imports and guards

* Add test, fix all the bugs exposed by said test

* Fix truncation of paired texts in graph mode, more test updates

* Small fixes, add a (very careful) test for savedmodel

* Add tensorflow-text dependency, make fixup

* Update documentation

* Update documentation

* make fixup

* Slight changes to tests

* Add some docstring examples

* Update tests

* Update tests and add proper lowercasing/normalization

* make fixup

* Add docstring for padding!

* Mark slow tests

* make fixup

* Fall back to BertTokenizerFast if BertTokenizer is unavailable

* Fall back to BertTokenizerFast if BertTokenizer is unavailable

* make fixup

* Properly handle tensorflow-text dummies

utils/check_dummies.py

@@ -26,7 +26,7 @@ PATH_TO_TRANSFORMERS = "src/transformers"
 _re_backend = re.compile(r"is\_([a-z_]*)_available()")
 # Matches from xxx import bla
 _re_single_line_import = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
-_re_test_backend = re.compile(r"^\s+if\s+not\s+is\_[a-z]*\_available\(\)")
+_re_test_backend = re.compile(r"^\s+if\s+not\s+is\_[a-z_]*\_available\(\)")
 
 
 DUMMY_CONSTANT = """